update gradio

app.py

@@ -25,8 +25,6 @@ device = 'cuda' if torch.cuda.is_available() else 'cpu'
 # Download official weights
 if not os.path.exists("saved_models"):
     os.mkdir("saved_models")
-    # MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn"
-    # gdown.download(MODEL_PATH_URL, "saved_models/isnet.pth", use_cookies=False)
     os.system("mv isnet.pth saved_models/")
     
 class GOSNormalize(object):
@@ -123,13 +121,13 @@ hypar["model"] = ISNetDIS()
 net = build_model(hypar, device)
 
 
-def inference(image: Image):
+def inference(image):
   image_path = image
   
   image_tensor, orig_size = load_image(image_path, hypar) 
   mask = predict(net, image_tensor, orig_size, hypar, device)
   
-  pil_mask = Image.fromarray(mask).convert("L")
+  pil_mask = Image.fromarray(mask).convert('L')
   im_rgb = Image.open(image).convert("RGB")
   
   im_rgba = im_rgb.copy()
@@ -140,7 +138,7 @@ def inference(image: Image):
 
 title = "Highly Accurate Dichotomous Image Segmentation"
 description = "This is an unofficial demo for DIS, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.<br>GitHub: https://github.com/xuebinqin/DIS<br>Telegram bot: https://t.me/restoration_photo_bot<br>[![](https://img.shields.io/twitter/follow/DoEvent?label=@DoEvent&style=social)](https://twitter.com/DoEvent)"
-article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=max_skobeev_dis_public' alt='visitor badge'></center></div>"
+article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=max_skobeev_dis_cmp_public' alt='visitor badge'></center></div>"
 
 interface = gr.Interface(
     fn=inference,
@@ -151,6 +149,5 @@ interface = gr.Interface(
     description=description,
     article=article,
     allow_flagging='never',
-    theme="default",
     cache_examples=False,
-    ).launch(enable_queue=True, debug=True)
+    ).queue(concurrency_count=1, api_open=True).launch(show_api=True, show_error=True)